2026-06-01T01:57:49Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/99292026-02-03T12:28:26Zcom_10630_2254col_10630_37959

A bottom-up robot architecture based on learnt behaviors driven design Herrero-Reder, Ignacio Urdiales-García, Amalia Cristina Peula-Palacios, José Manuel Sandoval-Hernández, Francisco Robótica In reactive layers of robotic architectures, behaviors should learn their operation from experience, following the trends of modern intelligence theories. A Case Based Reasoning (CBR) reactive layer could allow to achieve this goal but, as complexity of behaviors increases, thecurse of dimensionality arises: a too high amount of cases in the behaviors casebases deteriorate response times so robot's reactiveness is finally too slow for a good performance. In this work we analyze this problem and propose some improvements in the traditional CBR structure and retrieval phase, at reactive level, to reduce the impact of scalability problems when facing complex behaviors design. 2015-06-17T09:10:45Z 2015-06-17T09:10:45Z 2015-06-17T09:10:45Z 2015-06-17 conference output http://hdl.handle.net/10630/9929 orcid.org/0000-0001-9567-200X eng International Work Conference on Artificial Neural Networks (IWANN-2015) Mallorca, España 10/06/2015 open access by-nc-nd